Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942929
Sheng Chenxing, Z. Zongxin, Wang Huiyang, Han Yu
On-line detection of metallic wear debris is an effective approach for condition monitoring of mechanical systems. Existing on-line oil conditioning sensors are mainly based on ferrography and inductive techniques. However, ferrography technique needs a clean background and inductive technique requires a high cleanliness of lubricant. To solve these issues, in this paper a metallic wear debris sensor based on eddy current principle is developed. Both numerical simulations and prototype experiments are conducted to evaluate the capacity and feasibility of the new sensor for detecting wear debris. The analysis results demonstrate that: 1) A pulse is generated when the wear debris pass through the sensor, the amplitude and width of the pulse can be used to identify the material and size of the debris; 2) The developed sensor is able to detect copper debris with a diameter greater than 150 μm and iron debris greater than 60 μm. This work provides a new idea for detecting wear debris and a new method for obtaining the characteristics of wear debris.
{"title":"Development of Metallic Wear Debris Sensor Based on Eddy Current Technique","authors":"Sheng Chenxing, Z. Zongxin, Wang Huiyang, Han Yu","doi":"10.1109/phm-qingdao46334.2019.8942929","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942929","url":null,"abstract":"On-line detection of metallic wear debris is an effective approach for condition monitoring of mechanical systems. Existing on-line oil conditioning sensors are mainly based on ferrography and inductive techniques. However, ferrography technique needs a clean background and inductive technique requires a high cleanliness of lubricant. To solve these issues, in this paper a metallic wear debris sensor based on eddy current principle is developed. Both numerical simulations and prototype experiments are conducted to evaluate the capacity and feasibility of the new sensor for detecting wear debris. The analysis results demonstrate that: 1) A pulse is generated when the wear debris pass through the sensor, the amplitude and width of the pulse can be used to identify the material and size of the debris; 2) The developed sensor is able to detect copper debris with a diameter greater than 150 μm and iron debris greater than 60 μm. This work provides a new idea for detecting wear debris and a new method for obtaining the characteristics of wear debris.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124511023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8943014
Ruihua Jiao, Kai-xiang Peng, Jie Dong, Kai Zhang, Chuang-jian Zhang
Remaining useful life (RUL) prediction is of great importance in a successful prognostics and health management system. The performance of RUL prediction is mainly decided by the development of an appropriate health indicator (HI), which can accurately indicate the degree of degradation of the equipment. Therefore, we proposed an unsupervised method for HI construction based on deep belief network (DBN) by using multisensory historical data. Firstly, DBN is employed to describe the hidden representation corresponding to the healthy state. With the running of the system, its performance will decrease over time and the corresponding potential characteristics tend to be different. The deviation degree of degraded state can be used to establish HI so as to estimate the RUL. Finally, a case study is conducted to validate the effectiveness of the new method, where it can be seen that the new approach achieves better performance compared to traditional methods.
{"title":"A Health Indicator Construction Method based on Deep Belief Network for Remaining Useful Life Prediction","authors":"Ruihua Jiao, Kai-xiang Peng, Jie Dong, Kai Zhang, Chuang-jian Zhang","doi":"10.1109/phm-qingdao46334.2019.8943014","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943014","url":null,"abstract":"Remaining useful life (RUL) prediction is of great importance in a successful prognostics and health management system. The performance of RUL prediction is mainly decided by the development of an appropriate health indicator (HI), which can accurately indicate the degree of degradation of the equipment. Therefore, we proposed an unsupervised method for HI construction based on deep belief network (DBN) by using multisensory historical data. Firstly, DBN is employed to describe the hidden representation corresponding to the healthy state. With the running of the system, its performance will decrease over time and the corresponding potential characteristics tend to be different. The deviation degree of degraded state can be used to establish HI so as to estimate the RUL. Finally, a case study is conducted to validate the effectiveness of the new method, where it can be seen that the new approach achieves better performance compared to traditional methods.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124589487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8943040
Bingjun Guo, Lei Song, Taisheng Zheng, Haoran Liang, Hongfei Wang
Anomaly detection for multivariate data is of vital importance in academic research and industry. In real scenes, there is usually a lack of labels of anomalies. Self-Organizing Map (SOM) can map data to the output layer and maintain the original topology, which has been used as a semi-supervised learning method to solve the above problem. In this paper, we first explain the mechanism of classic SOM for anomaly detection, then compare it with two variants of SOM named kernel SOM and K-BMUs SOM. Kernel SOM replaces Euclidean distance with kernel functions, while K-BMUs SOM changes the number of matching neurons. The three types of SOM are applied to multivariate datasets in three different domains. We find that the performance of the three SOM-based methods is related to the characteristics of data.
{"title":"A Comparative Evaluation of SOM-based Anomaly Detection Methods for Multivariate Data","authors":"Bingjun Guo, Lei Song, Taisheng Zheng, Haoran Liang, Hongfei Wang","doi":"10.1109/phm-qingdao46334.2019.8943040","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943040","url":null,"abstract":"Anomaly detection for multivariate data is of vital importance in academic research and industry. In real scenes, there is usually a lack of labels of anomalies. Self-Organizing Map (SOM) can map data to the output layer and maintain the original topology, which has been used as a semi-supervised learning method to solve the above problem. In this paper, we first explain the mechanism of classic SOM for anomaly detection, then compare it with two variants of SOM named kernel SOM and K-BMUs SOM. Kernel SOM replaces Euclidean distance with kernel functions, while K-BMUs SOM changes the number of matching neurons. The three types of SOM are applied to multivariate datasets in three different domains. We find that the performance of the three SOM-based methods is related to the characteristics of data.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126630853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942871
Guangqi Qiu, Yingkui Gu
A methodology for developing dynamic diagnosis of multi-state degradation system was proposed in this paper. Wavelet packet energy entropy was employed to characterize the uncertainty and complexity of the signal. Current state evaluation and multi-state recognition had been implemented by hidden Markov model. The recognition performance was verified by a bearing vibration experiment, and the effects of decomposition levels and wavelet mother functions on the recognition performance were taken into account. Compared with classifiers of K-means, BP neural networks (BP-NN) and support vector machine (SVM), hidden Markov model (HMM) achieved a better recognition performance for multi-state degradation system and provided theoretical explanation of the system failure evolution.
{"title":"Dynamic Diagnosis Approach of Multi-state Degradation System Using Hidden Markov Model","authors":"Guangqi Qiu, Yingkui Gu","doi":"10.1109/phm-qingdao46334.2019.8942871","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942871","url":null,"abstract":"A methodology for developing dynamic diagnosis of multi-state degradation system was proposed in this paper. Wavelet packet energy entropy was employed to characterize the uncertainty and complexity of the signal. Current state evaluation and multi-state recognition had been implemented by hidden Markov model. The recognition performance was verified by a bearing vibration experiment, and the effects of decomposition levels and wavelet mother functions on the recognition performance were taken into account. Compared with classifiers of K-means, BP neural networks (BP-NN) and support vector machine (SVM), hidden Markov model (HMM) achieved a better recognition performance for multi-state degradation system and provided theoretical explanation of the system failure evolution.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126896146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942914
Yaohua Chen, C. Zhang, Ning Zhang, Yiting Chen, Huan Wang
The traction motor is one of the key components that plays an important role in ensuring the safety and stability of the running EMU (Electric Multiple Units). The running state of the traction motor can be determined through monitoring and predicting the change of EMU bearing temperature. In this paper, we propose a Long Short-Term Memory Neural Network based on Multi-task Learning and Attention Mechanism for the bearing temperature prediction in view of the complex influencing factors of bearing temperature in train operation. The model learns the characteristics of temperature sensors in different positions jointly through multi-task learning. And the Long Short-Term Memory Neural Network based on Attention Mechanism is used to consider the influence of current operating conditions and previous train records on bearing temperature in different degrees. So the model takes various influencing factors and spatial-temporal correlation into consideration. The experimental results with actual EMU datasets show that our method outperforms the baseline approaches.
{"title":"Multi-Task Learning and Attention Mechanism Based Long Short-Term Memory for Temperature Prediction of EMU Bearing","authors":"Yaohua Chen, C. Zhang, Ning Zhang, Yiting Chen, Huan Wang","doi":"10.1109/phm-qingdao46334.2019.8942914","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942914","url":null,"abstract":"The traction motor is one of the key components that plays an important role in ensuring the safety and stability of the running EMU (Electric Multiple Units). The running state of the traction motor can be determined through monitoring and predicting the change of EMU bearing temperature. In this paper, we propose a Long Short-Term Memory Neural Network based on Multi-task Learning and Attention Mechanism for the bearing temperature prediction in view of the complex influencing factors of bearing temperature in train operation. The model learns the characteristics of temperature sensors in different positions jointly through multi-task learning. And the Long Short-Term Memory Neural Network based on Attention Mechanism is used to consider the influence of current operating conditions and previous train records on bearing temperature in different degrees. So the model takes various influencing factors and spatial-temporal correlation into consideration. The experimental results with actual EMU datasets show that our method outperforms the baseline approaches.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125763311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8943017
{"title":"PHM-Qingdao 2019 Committee","authors":"","doi":"10.1109/phm-qingdao46334.2019.8943017","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943017","url":null,"abstract":"","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"232 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121868605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942862
Hongwei Luo, L. Song, Mengyang Wang, Huaqing Wang, Lingli Cui
In order to solve the difficulty of features extraction of compound faults in underdetermined state, this research proposes an approach to extract signal features by combining adaptive generalized S transform (GST) and non-negative matrix factorization algorithm (NMF). The adaptive function (AF) is introduced to optimize GST. The optimized GST is used to process monitored signals to get the time-frequency features matrix. The NMF is improved by Itakura-Saito (IS) divergence. And the dimensionality of the signal time-frequency matrix is reduced by it. After iterative updating, several low-dimensional matrices are obtained. The time-domain waveforms of low-dimensional matrices are reconstructed, and the envelope spectrum analysis is performed to realize compound faults diagnosis. The simulation test and the actual bearing compound fault signals experiment prove that this method can effectively extract compound fault features in underdetermined state and realize bearing compound faults diagnosis.
{"title":"Compound Faults Diagnosis Method Based on Adaptive GST-NMF for Rolling Bearing","authors":"Hongwei Luo, L. Song, Mengyang Wang, Huaqing Wang, Lingli Cui","doi":"10.1109/phm-qingdao46334.2019.8942862","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942862","url":null,"abstract":"In order to solve the difficulty of features extraction of compound faults in underdetermined state, this research proposes an approach to extract signal features by combining adaptive generalized S transform (GST) and non-negative matrix factorization algorithm (NMF). The adaptive function (AF) is introduced to optimize GST. The optimized GST is used to process monitored signals to get the time-frequency features matrix. The NMF is improved by Itakura-Saito (IS) divergence. And the dimensionality of the signal time-frequency matrix is reduced by it. After iterative updating, several low-dimensional matrices are obtained. The time-domain waveforms of low-dimensional matrices are reconstructed, and the envelope spectrum analysis is performed to realize compound faults diagnosis. The simulation test and the actual bearing compound fault signals experiment prove that this method can effectively extract compound fault features in underdetermined state and realize bearing compound faults diagnosis.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"679 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122974742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8943060
Shenghao Shi, Yongzhi Qu, Jinglin Wang, Liu Hong, J. Dhupia, Zude Zhou
The gearbox is one of the most common and important components in the drivetrains. Thus, the online monitoring of the dynamic behavior of geared system is crucial for the optimization, diagnosis and prognosis of the drivetrains. The conventional online monitoring system for gearboxes is to use the vibration sensor mounted on the gear housing. However, in the measured housing vibration signal, the dynamic response of the monitored geared pair is usually distorted, which is caused by the complex transfer path of the vibration. Therefore, to advance the art of online monitoring of gearboxes, this work proposes to employ the fiber Bragg grating as the strain sensor to mount near the gear mesh region. The experimental assessment of the feasibility of the fiber Bragg grating based online monitoring system is conducted in a laboratory fixed-axis spur gearbox. To validate and analyze the measurement from the fiber Bragg grating system, a gear mesh model is developed using the finite element method. The comparison between the measurement and theoretical simulation show the proposed fiber Bragg grating based online monitoring system is capable to capture the variation of the root strain during the gear mesh process. This result proves the proposed technique has a promising potential in developing a commercial online monitoring system to measure the subtle dynamic behavior of gearboxes.
{"title":"Feasibility Study of Online Monitoring Using the Fiber Bragg Grating Sensor for Geared System","authors":"Shenghao Shi, Yongzhi Qu, Jinglin Wang, Liu Hong, J. Dhupia, Zude Zhou","doi":"10.1109/phm-qingdao46334.2019.8943060","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943060","url":null,"abstract":"The gearbox is one of the most common and important components in the drivetrains. Thus, the online monitoring of the dynamic behavior of geared system is crucial for the optimization, diagnosis and prognosis of the drivetrains. The conventional online monitoring system for gearboxes is to use the vibration sensor mounted on the gear housing. However, in the measured housing vibration signal, the dynamic response of the monitored geared pair is usually distorted, which is caused by the complex transfer path of the vibration. Therefore, to advance the art of online monitoring of gearboxes, this work proposes to employ the fiber Bragg grating as the strain sensor to mount near the gear mesh region. The experimental assessment of the feasibility of the fiber Bragg grating based online monitoring system is conducted in a laboratory fixed-axis spur gearbox. To validate and analyze the measurement from the fiber Bragg grating system, a gear mesh model is developed using the finite element method. The comparison between the measurement and theoretical simulation show the proposed fiber Bragg grating based online monitoring system is capable to capture the variation of the root strain during the gear mesh process. This result proves the proposed technique has a promising potential in developing a commercial online monitoring system to measure the subtle dynamic behavior of gearboxes.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130078687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942948
Yajing Zhu, Zhinong Li, Jingzhi Tu
The traditional source number estimation method must ensure that the signal sources are independent and noise-free interference. Based on the above deficiency in the traditional BSS method, combining variational Bayesian hidden Markov model (VbHMM) and autocorrelation determination (ARD), a estimation method of mechanical fault sources number based on VbHMM is proposed. In the proposed method, after the Bayesian networks are introduced, the Markov models (HMM) is used to capture the characteristics of a series of time-related time series information in the dynamic and nonlinear signals. The optimal number of hidden sources in the non-stationary signal is deduced by the unique model comparison function of Bayesian inference and autocorrelation determination (ARD). Simulation and experimental results verify the effectiveness of the proposed method.
{"title":"Research on Estimation Method of Mechanical Fault Source Number Based on VbHMM","authors":"Yajing Zhu, Zhinong Li, Jingzhi Tu","doi":"10.1109/phm-qingdao46334.2019.8942948","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942948","url":null,"abstract":"The traditional source number estimation method must ensure that the signal sources are independent and noise-free interference. Based on the above deficiency in the traditional BSS method, combining variational Bayesian hidden Markov model (VbHMM) and autocorrelation determination (ARD), a estimation method of mechanical fault sources number based on VbHMM is proposed. In the proposed method, after the Bayesian networks are introduced, the Markov models (HMM) is used to capture the characteristics of a series of time-related time series information in the dynamic and nonlinear signals. The optimal number of hidden sources in the non-stationary signal is deduced by the unique model comparison function of Bayesian inference and autocorrelation determination (ARD). Simulation and experimental results verify the effectiveness of the proposed method.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129184613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Three-dimensional integrated packaging technology is recognized as the fourth generation packaging technology with the hope of breaking Moore's law. And through silicon via(TSV) technology is the key of three-dimensional packaging technology. In order to study the thermal-mechanical reliability of TSV structure, the finite element method was used to simulate the equivalent stress and deformation of TSV with different TSV size, aspect ratio, pitch and structure. The distribution of equivalent stress and deformation was obtained. The simulation results showed that the increase of TSV size would lead to the increase of equivalent stress and deformation, the aspect ratio of TSV would only affect deformation, and the increase of TSV pitch would lead to the decrease of equivalent stress and the increase of deformation. In addition, TSV filled with parylene was analyzed in this paper. The stress could be effectively released by increasing the size of parylene.
{"title":"Research on TSV Thermal-mechanical Reliability Based on Finite Element Analysis","authors":"Fangchao Huang, Zhengwei Fan, Xun Chen, Yao Liu, Shufeng Zhang, Yashun Wang, Yu Jiang","doi":"10.1109/phm-qingdao46334.2019.8942816","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942816","url":null,"abstract":"Three-dimensional integrated packaging technology is recognized as the fourth generation packaging technology with the hope of breaking Moore's law. And through silicon via(TSV) technology is the key of three-dimensional packaging technology. In order to study the thermal-mechanical reliability of TSV structure, the finite element method was used to simulate the equivalent stress and deformation of TSV with different TSV size, aspect ratio, pitch and structure. The distribution of equivalent stress and deformation was obtained. The simulation results showed that the increase of TSV size would lead to the increase of equivalent stress and deformation, the aspect ratio of TSV would only affect deformation, and the increase of TSV pitch would lead to the decrease of equivalent stress and the increase of deformation. In addition, TSV filled with parylene was analyzed in this paper. The stress could be effectively released by increasing the size of parylene.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122947415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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